mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-11-13 22:56:18 +00:00
70 lines
1.6 KiB
Python
70 lines
1.6 KiB
Python
# Titan Robotics Team 2022: CorrelationTest submodule
|
|
# Written by Arthur Lu
|
|
# Notes:
|
|
# this should be imported as a python module using 'from tra_analysis import CorrelationTest'
|
|
# setup:
|
|
|
|
__version__ = "1.0.3"
|
|
|
|
__changelog__ = """changelog:
|
|
1.0.3:
|
|
- generalized optional args to **kwargs
|
|
1.0.2:
|
|
- optimized imports
|
|
1.0.1:
|
|
- fixed __all__
|
|
1.0.0:
|
|
- ported analysis.CorrelationTest() here
|
|
- removed classness
|
|
"""
|
|
|
|
__author__ = (
|
|
"Arthur Lu <learthurgo@gmail.com>",
|
|
)
|
|
|
|
__all__ = [
|
|
"anova_oneway",
|
|
"pearson",
|
|
"spearman",
|
|
"point_biserial",
|
|
"kendall",
|
|
"kendall_weighted",
|
|
"mgc",
|
|
]
|
|
|
|
import scipy
|
|
|
|
def anova_oneway(*args): #expects arrays of samples
|
|
|
|
results = scipy.stats.f_oneway(*args)
|
|
return {"f-value": results[0], "p-value": results[1]}
|
|
|
|
def pearson(x, y):
|
|
|
|
results = scipy.stats.pearsonr(x, y)
|
|
return {"r-value": results[0], "p-value": results[1]}
|
|
|
|
def spearman(a, b = None, **kwargs):
|
|
|
|
results = scipy.stats.spearmanr(a, b = b, **kwargs)
|
|
return {"r-value": results[0], "p-value": results[1]}
|
|
|
|
def point_biserial(x, y):
|
|
|
|
results = scipy.stats.pointbiserialr(x, y)
|
|
return {"r-value": results[0], "p-value": results[1]}
|
|
|
|
def kendall(x, y, **kwargs):
|
|
|
|
results = scipy.stats.kendalltau(x, y, **kwargs)
|
|
return {"tau": results[0], "p-value": results[1]}
|
|
|
|
def kendall_weighted(x, y, **kwargs):
|
|
|
|
results = scipy.stats.weightedtau(x, y, **kwargs)
|
|
return {"tau": results[0], "p-value": results[1]}
|
|
|
|
def mgc(x, y, **kwargs):
|
|
|
|
results = scipy.stats.multiscale_graphcorr(x, y, **kwargs)
|
|
return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value |